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BGA算法在备件库存控制策略优化中的应用研究

张守京 秦小凡

张守京,秦小凡. BGA算法在备件库存控制策略优化中的应用研究[J]. 机械科学与技术,2021,40(4):649-656 doi: 10.13433/j.cnki.1003-8728.20200088
引用本文: 张守京,秦小凡. BGA算法在备件库存控制策略优化中的应用研究[J]. 机械科学与技术,2021,40(4):649-656 doi: 10.13433/j.cnki.1003-8728.20200088
ZHANG Shoujing, QIN Xiaofan. Application of BGA Algorithm in Optimization of Spare Parts Inventory Control Strategy[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(4): 649-656. doi: 10.13433/j.cnki.1003-8728.20200088
Citation: ZHANG Shoujing, QIN Xiaofan. Application of BGA Algorithm in Optimization of Spare Parts Inventory Control Strategy[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(4): 649-656. doi: 10.13433/j.cnki.1003-8728.20200088

BGA算法在备件库存控制策略优化中的应用研究

doi: 10.13433/j.cnki.1003-8728.20200088
基金项目: 西安市现代智能纺织装备重点实验室(2019220614SYS021CG043)、陕西省教育厅科研计划项目(No.17JK0321)及中国纺织联合会项目(No.2017100)
详细信息
    作者简介:

    张守京(1976−),副教授,硕士生导师,研究方向为智能制造技术及系统、智慧物流与柔性生产调度,zhangshoujing@xpu.edu.cn

  • 中图分类号: TG156

Application of BGA Algorithm in Optimization of Spare Parts Inventory Control Strategy

  • 摘要: 建立一个考虑备件重要度库存控制模型,并运用提出的BGA算法(BAS-Genetic algorithm)实现运算收敛快,获得总成本更优。首先以库存成本最小化为原则,建立库存模型的目标函数,其次建立了以基于重要度的服务水平为约束条件,构建库存控制模型。最后根据考虑维修备件重要度的库存控制策略模型。提出BGA(BAS-Genetic algorithm)算法用于最优库存控制策略解的查找。结果表明,BGA算法的收敛比GA(Genetic algorithm)收敛的速度快,且计算的目标函数成本值更小,不易陷入局部最优。
  • 图  1  库存水平状态转移图

    图  2  BGA算法流程图

    图  3  父代染色体

    图  4  染色体交叉

    图  5  染色体变异

    图  6  GA结果

    图  7  BGA结果

    表  1  不同μIm参数控制下的敏感性分析

    μTC ,(r, S
    Im = 0.1Im = 0.2Im = 0.3Im = 0.4
    10 1029.074074,(17,30) 1137.289157,(43,167) 1175.83004,(46,172) 1596.392111,(47,262)
    20 1019.336735,(11,23) 1038.928571,(13,25) 1077.675439,(28,42) 1133.932741,(41,169)
    30 936.826484,(3,15) 1015.730594,(11,23) 1025.509804,(23,37) 1036.199621,(36,153)
    40 924.8324742,(2,14) 974.3170103,(7,19) 1004.14823,(21,35) 1012.180659,(33,50)
    下载: 导出CSV

    表  2  Im=0.4,λ =5,μ参数控制下的成本敏感度分析

    μReShI
    10 0.023 1.64029×1043 154.748
    20 0.038 8.06219×1055 105.373
    30 0.042 3.00072×1059 94.916
    40 0.278 2.78834×1062 41.933
    下载: 导出CSV

    表  3  不同λIm参数控制下的敏感性分析

    λTC ,( r, S
    Im = 0.1Im = 0.2Im = 0.3Im = 0.4
    10 3299.1667,(6,12) 3305.595238,(7,13) 3324.880952,(10,16) 3376.309524,(18,24)
    20 3685.09803,(8,24) 4238.587571,(12,25) 4587.261905,(33,45) 4602.146893,(67,80)
    30 5547.5,(9,27) 5894.074074,(33,50) 6029.642857,(84,102) 6151.785714,(103,121)
    40 7234.53074,(12,33) 7486.611577,(63,75) 7694.3017,(136,149) 7733.38374,(140,153)
    下载: 导出CSV

    表  4  Im=0.4,μ=3,λ参数控制下的成本敏感度分析

    λReShI
    10 1.42857142857140 13549.51988 19.05952381
    20 1.42857142857143 138296072.32923 69.36723164
    30 1.57894736842105 360459408.71955 105.5357143
    40 2.85714285714286 946071904.02461 136.7046414
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-01-14
  • 网络出版日期:  2021-04-16
  • 刊出日期:  2021-04-16

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